This application is related to a co-pending application; Ser. No. 371,355 filed Apr. 23, 1982; entitled Digital Gaussian Convolver wherein the sole inventor in subject patent application is one of the co-inventors and the assignee of both patent applications is the same.
This invention is related to vision systems and, more particularly, to an image analysis using edge detection which is accomplished by means of an analog convolver.
As pointed out by Hildreth in the September/October 1981 issue of Robotics Age which is herein incorporated by reference, most vision systems begin by finding edges. This is true of a human vision system and can be the case for a machine made vision system. In the case of human vision system, it first registers light intensity with the array of photo receptors in the retina and the image is sensed at a high resolution. As an example, one square inch, viewed from a distance of 3 feet, covers an array of about 200.times.200 to 40,000 photo receptors. Several layers of cells in the retina process the detected light intensity. It has been found that light striking the center of the cell's receptive field excites the activity of the cell while light striking the surrounding area inhibits it. The variation of the sensitivity of cells a vision system has been studied and the shape of sensitivity distribution has been described mathematically as the difference of two concentric Gaussian distributions given by: ##EQU1## where r is the radius from the center and s.sub.1 and s.sub.2 are the spatial scale factors of the excitatory and inhibitory distributions, respectively. It is the shape of this distribution which is considered significant rather than its magnitude. It has been suggested that the processing of the information done in the retina is non-oriented with the simple cells processing the results to find edges, i.e. significant intensity changes in an image.
The above-mentioned ideas have been put forward by Marr and Poggio in their M.I.T. Artificial Intelligence Laboratory Memo 451; (November 1977) Marr and Hildreth in their M.I.T. Artificial Intelligence Laboratory Memo 518 (1979) and by W. E. L. Grimson in M.I.T. Artificial Intelligence Laboratory Memo 565 (January 1980) which are herein incorporated by reference. These ideas have been used in image processing and computer vision in conjunction with the mathematical process of convolution using a Gaussian function.
One approach to edge detection proceeds in two steps: First the image is smoothed (low pass filtered) by convolving it with a 2-dimensional Gaussian operator. The purpose of this is to control the scale at which subsequent processes, such as stereo matching, are performed. The filtered image can be represented mathematically by: EQU I.sub.out (x,y)=I.sub.in (x,y)*G(x,y)
The next step is to detect edge-related features in the filtered image. This can be accomplished by differentiating it, using a 2-dimensional Laplacian Operator, and finding the zero-crossings in the resulting function.
It can be shown, mathematically, that the Laplacian operator applied to a Gaussian operator can be approximated by a difference of Gaussians, similar to the mechanism described above, in reference to the human vision system.
It is thus desirable to have a device which performs the above mentioned two steps to process image by using computer vision involving edge detection.